Analysis of Students Dropout Forecasting Using Data Mining

Education is the backbone of any country and it is very important to improve the educational strength of the country. There are various methods and challenges on the way, use of technologies like computers, smart rooms, projectors, and eBooks. But these resources are useful only when we know which student needs which type of resource or, in other words, if we are able to predict the results of students, we can improve results and decrease drop out ratio. In our research, we used Data Mining in education to improve the results of the schools. As we know large amount of data is stored in educational database, so in order to get required data & to find the hidden relationship, different data mining techniques are developed & used. There are varieties of popular data mining task within the educational data mining e.g. classification, clustering, outlier detection, association rule, prediction etc.How each of data mining tasks can be applied to education system is explained. To predict the failure of students is a complex task, as it requires large number of the data to be handled. For which the record of students, their each and every activities, academic related information need to maintain. Based on this information, it will be easier to predict the student’s failure by applying data mining algorithms on it. The final objective of this paper is to detect the failure of students as early as possible to prevent them from dropping out and improve their academic performance. The outcomes are compared, the models with bestresults can be shown and the students who are at risk of failure can be provided with the guidance.